Overview

Dataset statistics

Number of variables26
Number of observations843
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory171.4 KiB
Average record size in memory208.2 B

Variable types

Text1
Categorical8
DateTime2
Numeric15

Alerts

size_optimised has constant value ""Constant
length is highly overall correlated with date and 13 other fieldsHigh correlation
mean_intensity_calculated is highly overall correlated with defect_type and 10 other fieldsHigh correlation
size_calculated is highly overall correlated with date and 13 other fieldsHigh correlation
porosity_calculated is highly overall correlated with thickness_calculated and 1 other fieldsHigh correlation
length_calculated is highly overall correlated with defect_type and 8 other fieldsHigh correlation
width_calculated is highly overall correlated with defect_type and 12 other fieldsHigh correlation
thickness_calculated is highly overall correlated with date and 13 other fieldsHigh correlation
threshold_optimised is highly overall correlated with date and 12 other fieldsHigh correlation
area is highly overall correlated with date and 14 other fieldsHigh correlation
area_calculated is highly overall correlated with date and 14 other fieldsHigh correlation
length_width_ratio is highly overall correlated with date and 13 other fieldsHigh correlation
length_width_ratio_calculated is highly overall correlated with length_calculated and 1 other fieldsHigh correlation
size_length_width_ratio is highly overall correlated with date and 15 other fieldsHigh correlation
size_length_width_ratio_calculated is highly overall correlated with porosity_calculated and 2 other fieldsHigh correlation
defect_type is highly overall correlated with thickness and 2 other fieldsHigh correlation
detection_mode is highly overall correlated with date and 3 other fieldsHigh correlation
width is highly overall correlated with thickness and 2 other fieldsHigh correlation
thickness is highly overall correlated with defect_type and 2 other fieldsHigh correlation
defect_family is highly overall correlated with thickness and 2 other fieldsHigh correlation
date is highly overall correlated with defect_type and 13 other fieldsHigh correlation
product is highly overall correlated with date and 2 other fieldsHigh correlation
thickness is highly imbalanced (50.0%)Imbalance
file_name has unique valuesUnique
mean_intensity_calculated has unique valuesUnique
thickness_calculated has unique valuesUnique

Reproduction

Analysis started2023-06-14 20:51:11.248982
Analysis finished2023-06-14 20:51:40.499531
Duration29.25 seconds
Software versionydata-profiling vv4.2.0
Download configurationconfig.json

Variables

Distinct843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:40.590195image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters29505
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique843 ?
Unique (%)100.0%

Sample

1st rowok112009_2023-01-05-08-09-58-376137
2nd rowok112009_2023-01-05-08-10-54-621560
3rd rowok112009_2023-01-05-08-11-33-694801
4th rowok112009_2023-01-05-08-12-12-745345
5th rowok112009_2023-01-05-08-12-53-064465
ValueCountFrequency (%)
ok112009_2023-01-05-08-09-58-376137 1
 
0.1%
ok112009_2023-01-20-08-32-55-653021 1
 
0.1%
ok112009_2023-01-05-09-28-45-831791 1
 
0.1%
ok112009_2023-01-05-08-27-48-085077 1
 
0.1%
ok112009_2023-01-05-08-11-33-694801 1
 
0.1%
ok112009_2023-01-05-08-12-12-745345 1
 
0.1%
ok112009_2023-01-05-08-12-53-064465 1
 
0.1%
ok112009_2023-01-05-08-13-23-744552 1
 
0.1%
ok112009_2023-01-05-08-14-07-130031 1
 
0.1%
ok112009_2023-01-05-08-14-47-436262 1
 
0.1%
Other values (833) 833
98.8%
2023-06-14T22:51:40.849623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5082
17.2%
- 4571
15.5%
2 4558
15.4%
1 3866
13.1%
3 2630
8.9%
9 1433
 
4.9%
_ 1330
 
4.5%
4 997
 
3.4%
5 952
 
3.2%
8 916
 
3.1%
Other values (4) 3170
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21918
74.3%
Dash Punctuation 4571
 
15.5%
Lowercase Letter 1686
 
5.7%
Connector Punctuation 1330
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5082
23.2%
2 4558
20.8%
1 3866
17.6%
3 2630
12.0%
9 1433
 
6.5%
4 997
 
4.5%
5 952
 
4.3%
8 916
 
4.2%
6 755
 
3.4%
7 729
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
o 843
50.0%
k 843
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 4571
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1330
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 27819
94.3%
Latin 1686
 
5.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5082
18.3%
- 4571
16.4%
2 4558
16.4%
1 3866
13.9%
3 2630
9.5%
9 1433
 
5.2%
_ 1330
 
4.8%
4 997
 
3.6%
5 952
 
3.4%
8 916
 
3.3%
Other values (2) 1484
 
5.3%
Latin
ValueCountFrequency (%)
o 843
50.0%
k 843
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 29505
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5082
17.2%
- 4571
15.5%
2 4558
15.4%
1 3866
13.1%
3 2630
8.9%
9 1433
 
4.9%
_ 1330
 
4.5%
4 997
 
3.4%
5 952
 
3.2%
8 916
 
3.1%
Other values (4) 3170
10.7%

scanner_id
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
ok112009
527 
ok112013
316 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters6744
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowok112009
2nd rowok112009
3rd rowok112009
4th rowok112009
5th rowok112009

Common Values

ValueCountFrequency (%)
ok112009 527
62.5%
ok112013 316
37.5%

Length

2023-06-14T22:51:40.971974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:41.078728image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ok112009 527
62.5%
ok112013 316
37.5%

Most occurring characters

ValueCountFrequency (%)
1 2002
29.7%
0 1370
20.3%
o 843
12.5%
k 843
12.5%
2 843
12.5%
9 527
 
7.8%
3 316
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5058
75.0%
Lowercase Letter 1686
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2002
39.6%
0 1370
27.1%
2 843
16.7%
9 527
 
10.4%
3 316
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
o 843
50.0%
k 843
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 5058
75.0%
Latin 1686
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2002
39.6%
0 1370
27.1%
2 843
16.7%
9 527
 
10.4%
3 316
 
6.2%
Latin
ValueCountFrequency (%)
o 843
50.0%
k 843
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6744
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2002
29.7%
0 1370
20.3%
o 843
12.5%
k 843
12.5%
2 843
12.5%
9 527
 
7.8%
3 316
 
4.7%

date
Date

Distinct17
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Minimum2023-01-05 00:00:00
Maximum2023-03-24 00:00:00
2023-06-14T22:51:41.155336image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:41.238581image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=17)

time
Date

Distinct806
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Minimum2023-06-14 07:53:40
Maximum2023-06-14 15:37:06
2023-06-14T22:51:41.343448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:41.447920image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

defect_type
Categorical

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
CG
459 
MeltGros
135 
Melt
132 
CGGros
117 

Length

Max length8
Median length2
Mean length3.8291815
Min length2

Characters and Unicode

Total characters3228
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCG
2nd rowCG
3rd rowCG
4th rowCG
5th rowCG

Common Values

ValueCountFrequency (%)
CG 459
54.4%
MeltGros 135
 
16.0%
Melt 132
 
15.7%
CGGros 117
 
13.9%

Length

2023-06-14T22:51:41.550656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:41.654555image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cg 459
54.4%
meltgros 135
 
16.0%
melt 132
 
15.7%
cggros 117
 
13.9%

Most occurring characters

ValueCountFrequency (%)
G 828
25.7%
C 576
17.8%
M 267
 
8.3%
e 267
 
8.3%
l 267
 
8.3%
t 267
 
8.3%
r 252
 
7.8%
o 252
 
7.8%
s 252
 
7.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 1671
51.8%
Lowercase Letter 1557
48.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 267
17.1%
l 267
17.1%
t 267
17.1%
r 252
16.2%
o 252
16.2%
s 252
16.2%
Uppercase Letter
ValueCountFrequency (%)
G 828
49.6%
C 576
34.5%
M 267
 
16.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3228
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
G 828
25.7%
C 576
17.8%
M 267
 
8.3%
e 267
 
8.3%
l 267
 
8.3%
t 267
 
8.3%
r 252
 
7.8%
o 252
 
7.8%
s 252
 
7.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3228
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
G 828
25.7%
C 576
17.8%
M 267
 
8.3%
e 267
 
8.3%
l 267
 
8.3%
t 267
 
8.3%
r 252
 
7.8%
o 252
 
7.8%
s 252
 
7.8%

product
Categorical

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
Ekla20
468 
Pacific12
251 
Arctic15
124 

Length

Max length9
Median length6
Mean length7.1874259
Min length6

Characters and Unicode

Total characters6059
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEkla20
2nd rowEkla20
3rd rowEkla20
4th rowEkla20
5th rowEkla20

Common Values

ValueCountFrequency (%)
Ekla20 468
55.5%
Pacific12 251
29.8%
Arctic15 124
 
14.7%

Length

2023-06-14T22:51:41.747708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:41.849507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
ekla20 468
55.5%
pacific12 251
29.8%
arctic15 124
 
14.7%

Most occurring characters

ValueCountFrequency (%)
c 750
12.4%
a 719
11.9%
2 719
11.9%
i 626
10.3%
E 468
7.7%
k 468
7.7%
l 468
7.7%
0 468
7.7%
1 375
6.2%
P 251
 
4.1%
Other values (5) 747
12.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3530
58.3%
Decimal Number 1686
27.8%
Uppercase Letter 843
 
13.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
c 750
21.2%
a 719
20.4%
i 626
17.7%
k 468
13.3%
l 468
13.3%
f 251
 
7.1%
r 124
 
3.5%
t 124
 
3.5%
Decimal Number
ValueCountFrequency (%)
2 719
42.6%
0 468
27.8%
1 375
22.2%
5 124
 
7.4%
Uppercase Letter
ValueCountFrequency (%)
E 468
55.5%
P 251
29.8%
A 124
 
14.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 4373
72.2%
Common 1686
 
27.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
c 750
17.2%
a 719
16.4%
i 626
14.3%
E 468
10.7%
k 468
10.7%
l 468
10.7%
P 251
 
5.7%
f 251
 
5.7%
A 124
 
2.8%
r 124
 
2.8%
Common
ValueCountFrequency (%)
2 719
42.6%
0 468
27.8%
1 375
22.2%
5 124
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6059
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
c 750
12.4%
a 719
11.9%
2 719
11.9%
i 626
10.3%
E 468
7.7%
k 468
7.7%
l 468
7.7%
0 468
7.7%
1 375
6.2%
P 251
 
4.1%
Other values (5) 747
12.3%

detection_mode
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
manual
732 
automatic
111 

Length

Max length9
Median length6
Mean length6.3950178
Min length6

Characters and Unicode

Total characters5391
Distinct characters9
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmanual
2nd rowmanual
3rd rowmanual
4th rowmanual
5th rowmanual

Common Values

ValueCountFrequency (%)
manual 732
86.8%
automatic 111
 
13.2%

Length

2023-06-14T22:51:41.943585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:42.047456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
manual 732
86.8%
automatic 111
 
13.2%

Most occurring characters

ValueCountFrequency (%)
a 1686
31.3%
m 843
15.6%
u 843
15.6%
n 732
13.6%
l 732
13.6%
t 222
 
4.1%
o 111
 
2.1%
i 111
 
2.1%
c 111
 
2.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5391
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1686
31.3%
m 843
15.6%
u 843
15.6%
n 732
13.6%
l 732
13.6%
t 222
 
4.1%
o 111
 
2.1%
i 111
 
2.1%
c 111
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 5391
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1686
31.3%
m 843
15.6%
u 843
15.6%
n 732
13.6%
l 732
13.6%
t 222
 
4.1%
o 111
 
2.1%
i 111
 
2.1%
c 111
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5391
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1686
31.3%
m 843
15.6%
u 843
15.6%
n 732
13.6%
l 732
13.6%
t 222
 
4.1%
o 111
 
2.1%
i 111
 
2.1%
c 111
 
2.1%

length
Real number (ℝ)

Distinct6
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0326216
Minimum2
Maximum6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:42.127692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median2.5
Q33.5
95-th percentile6
Maximum6
Range4
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation1.3831583
Coefficient of variation (CV)0.45609328
Kurtosis0.36860792
Mean3.0326216
Median Absolute Deviation (MAD)0.5
Skewness1.293966
Sum2556.5
Variance1.913127
MonotonicityNot monotonic
2023-06-14T22:51:42.210078image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 419
49.7%
3.5 135
 
16.0%
3 128
 
15.2%
6 127
 
15.1%
2.5 24
 
2.8%
4 10
 
1.2%
ValueCountFrequency (%)
2 419
49.7%
2.5 24
 
2.8%
3 128
 
15.2%
3.5 135
 
16.0%
4 10
 
1.2%
6 127
 
15.1%
ValueCountFrequency (%)
6 127
 
15.1%
4 10
 
1.2%
3.5 135
 
16.0%
3 128
 
15.2%
2.5 24
 
2.8%
2 419
49.7%

width
Categorical

Distinct5
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
2.0
428 
3.0
198 
5.5
137 
2.5
70 
3.5
 
10

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2529
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.5
2nd row2.5
3rd row2.5
4th row2.5
5th row2.5

Common Values

ValueCountFrequency (%)
2.0 428
50.8%
3.0 198
23.5%
5.5 137
 
16.3%
2.5 70
 
8.3%
3.5 10
 
1.2%

Length

2023-06-14T22:51:42.471277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:42.575605image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
2.0 428
50.8%
3.0 198
23.5%
5.5 137
 
16.3%
2.5 70
 
8.3%
3.5 10
 
1.2%

Most occurring characters

ValueCountFrequency (%)
. 843
33.3%
0 626
24.8%
2 498
19.7%
5 354
14.0%
3 208
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1686
66.7%
Other Punctuation 843
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 626
37.1%
2 498
29.5%
5 354
21.0%
3 208
 
12.3%
Other Punctuation
ValueCountFrequency (%)
. 843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 843
33.3%
0 626
24.8%
2 498
19.7%
5 354
14.0%
3 208
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 843
33.3%
0 626
24.8%
2 498
19.7%
5 354
14.0%
3 208
 
8.2%

thickness
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
1.0
673 
1.4
95 
1.5
 
55
0.9
 
20

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2529
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 673
79.8%
1.4 95
 
11.3%
1.5 55
 
6.5%
0.9 20
 
2.4%

Length

2023-06-14T22:51:42.673641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:42.773732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1.0 673
79.8%
1.4 95
 
11.3%
1.5 55
 
6.5%
0.9 20
 
2.4%

Most occurring characters

ValueCountFrequency (%)
. 843
33.3%
1 823
32.5%
0 693
27.4%
4 95
 
3.8%
5 55
 
2.2%
9 20
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1686
66.7%
Other Punctuation 843
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 823
48.8%
0 693
41.1%
4 95
 
5.6%
5 55
 
3.3%
9 20
 
1.2%
Other Punctuation
ValueCountFrequency (%)
. 843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 843
33.3%
1 823
32.5%
0 693
27.4%
4 95
 
3.8%
5 55
 
2.2%
9 20
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 843
33.3%
1 823
32.5%
0 693
27.4%
4 95
 
3.8%
5 55
 
2.2%
9 20
 
0.8%

mean_intensity_calculated
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17703545
Minimum0.11716446
Maximum0.23968609
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:42.870402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.11716446
5-th percentile0.13795603
Q10.16414995
median0.17830998
Q30.19208847
95-th percentile0.21221202
Maximum0.23968609
Range0.12252163
Interquartile range (IQR)0.027938515

Descriptive statistics

Standard deviation0.022077451
Coefficient of variation (CV)0.12470639
Kurtosis-0.27021201
Mean0.17703545
Median Absolute Deviation (MAD)0.013931513
Skewness-0.20670681
Sum149.24088
Variance0.00048741384
MonotonicityNot monotonic
2023-06-14T22:51:42.984671image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1974007934 1
 
0.1%
0.1674735844 1
 
0.1%
0.1856275797 1
 
0.1%
0.1601466835 1
 
0.1%
0.1786067635 1
 
0.1%
0.1800111979 1
 
0.1%
0.1861017197 1
 
0.1%
0.1746126562 1
 
0.1%
0.1707176268 1
 
0.1%
0.1935824603 1
 
0.1%
Other values (833) 833
98.8%
ValueCountFrequency (%)
0.1171644554 1
0.1%
0.1203410178 1
0.1%
0.1246931031 1
0.1%
0.1254417747 1
0.1%
0.1263875663 1
0.1%
0.1264971793 1
0.1%
0.1267471761 1
0.1%
0.1270685345 1
0.1%
0.1279088855 1
0.1%
0.1280268133 1
0.1%
ValueCountFrequency (%)
0.2396860868 1
0.1%
0.2360898554 1
0.1%
0.2348875254 1
0.1%
0.2334230691 1
0.1%
0.2315942049 1
0.1%
0.2251617759 1
0.1%
0.2238425612 1
0.1%
0.2236688584 1
0.1%
0.2234222293 1
0.1%
0.2230705768 1
0.1%

size_calculated
Real number (ℝ)

Distinct379
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean359.69988
Minimum150
Maximum904
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:43.109882image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum150
5-th percentile196
Q1248
median307
Q3401
95-th percentile769.8
Maximum904
Range754
Interquartile range (IQR)153

Descriptive statistics

Standard deviation163.82695
Coefficient of variation (CV)0.45545456
Kurtosis1.4509682
Mean359.69988
Median Absolute Deviation (MAD)69
Skewness1.4927545
Sum303227
Variance26839.27
MonotonicityNot monotonic
2023-06-14T22:51:43.218511image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
281 9
 
1.1%
247 8
 
0.9%
271 8
 
0.9%
280 8
 
0.9%
231 8
 
0.9%
228 7
 
0.8%
224 7
 
0.8%
336 7
 
0.8%
255 7
 
0.8%
208 7
 
0.8%
Other values (369) 767
91.0%
ValueCountFrequency (%)
150 1
0.1%
151 1
0.1%
157 1
0.1%
163 1
0.1%
167 1
0.1%
169 1
0.1%
172 1
0.1%
174 1
0.1%
176 1
0.1%
178 2
0.2%
ValueCountFrequency (%)
904 1
0.1%
889 1
0.1%
848 1
0.1%
847 1
0.1%
841 1
0.1%
835 1
0.1%
834 1
0.1%
832 2
0.2%
829 1
0.1%
826 1
0.1%

porosity_calculated
Real number (ℝ)

Distinct799
Distinct (%)94.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.18502115
Minimum0.054187192
Maximum0.36995153
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:43.333675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.054187192
5-th percentile0.10288818
Q10.14204348
median0.17910448
Q30.22085873
95-th percentile0.28818432
Maximum0.36995153
Range0.31576434
Interquartile range (IQR)0.078815245

Descriptive statistics

Standard deviation0.056853469
Coefficient of variation (CV)0.30728092
Kurtosis0.022081045
Mean0.18502115
Median Absolute Deviation (MAD)0.0387536
Skewness0.52758466
Sum155.97283
Variance0.003232317
MonotonicityNot monotonic
2023-06-14T22:51:43.438971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1666666667 6
 
0.7%
0.2322946176 3
 
0.4%
0.1111111111 3
 
0.4%
0.1739130435 3
 
0.4%
0.1346153846 3
 
0.4%
0.1515151515 3
 
0.4%
0.2307692308 3
 
0.4%
0.1607717042 2
 
0.2%
0.1275720165 2
 
0.2%
0.1450381679 2
 
0.2%
Other values (789) 813
96.4%
ValueCountFrequency (%)
0.05418719212 1
0.1%
0.06329113924 1
0.1%
0.06382978723 1
0.1%
0.07266009852 1
0.1%
0.07522123894 1
0.1%
0.07602339181 1
0.1%
0.07835820896 1
0.1%
0.07971014493 1
0.1%
0.08016032064 1
0.1%
0.08148148148 1
0.1%
ValueCountFrequency (%)
0.3699515347 1
0.1%
0.3621621622 1
0.1%
0.3608490566 1
0.1%
0.358490566 1
0.1%
0.3573369565 1
0.1%
0.3558648111 1
0.1%
0.3426666667 1
0.1%
0.3347639485 1
0.1%
0.3313953488 1
0.1%
0.3311258278 1
0.1%

length_calculated
Real number (ℝ)

Distinct30
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.984579
Minimum8
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:43.540745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile13
Q115
median18
Q322
95-th percentile29
Maximum37
Range29
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.0252096
Coefficient of variation (CV)0.26469955
Kurtosis0.86529447
Mean18.984579
Median Absolute Deviation (MAD)3
Skewness0.97555425
Sum16004
Variance25.252731
MonotonicityNot monotonic
2023-06-14T22:51:43.624944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
18 90
10.7%
17 83
 
9.8%
16 82
 
9.7%
15 78
 
9.3%
14 74
 
8.8%
19 65
 
7.7%
20 45
 
5.3%
22 42
 
5.0%
21 37
 
4.4%
24 33
 
3.9%
Other values (20) 214
25.4%
ValueCountFrequency (%)
8 1
 
0.1%
9 4
 
0.5%
10 2
 
0.2%
11 11
 
1.3%
12 17
 
2.0%
13 32
 
3.8%
14 74
8.8%
15 78
9.3%
16 82
9.7%
17 83
9.8%
ValueCountFrequency (%)
37 2
 
0.2%
36 2
 
0.2%
35 3
 
0.4%
34 3
 
0.4%
33 7
 
0.8%
32 6
 
0.7%
31 8
0.9%
30 9
1.1%
29 5
 
0.6%
28 19
2.3%

width_calculated
Real number (ℝ)

Distinct39
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.946619
Minimum12
Maximum54
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:43.724641image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile21
Q126
median28
Q333
95-th percentile44
Maximum54
Range42
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.7509653
Coefficient of variation (CV)0.2254333
Kurtosis0.47814069
Mean29.946619
Median Absolute Deviation (MAD)3
Skewness0.90559747
Sum25245
Variance45.575532
MonotonicityNot monotonic
2023-06-14T22:51:43.824631image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
28 84
 
10.0%
27 80
 
9.5%
29 72
 
8.5%
26 65
 
7.7%
30 51
 
6.0%
25 47
 
5.6%
31 44
 
5.2%
24 39
 
4.6%
23 37
 
4.4%
32 34
 
4.0%
Other values (29) 290
34.4%
ValueCountFrequency (%)
12 1
 
0.1%
15 1
 
0.1%
16 1
 
0.1%
17 3
 
0.4%
18 2
 
0.2%
19 8
 
0.9%
20 9
 
1.1%
21 21
2.5%
22 32
3.8%
23 37
4.4%
ValueCountFrequency (%)
54 1
 
0.1%
52 1
 
0.1%
51 2
 
0.2%
49 1
 
0.1%
48 6
 
0.7%
47 7
0.8%
46 5
 
0.6%
45 9
1.1%
44 16
1.9%
43 14
1.7%

thickness_calculated
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct843
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.04
Minimum11.291065
Maximum20.03643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:43.939393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum11.291065
5-th percentile12.773178
Q113.902029
median14.876008
Q315.98179
95-th percentile18.133132
Maximum20.03643
Range8.7453651
Interquartile range (IQR)2.079761

Descriptive statistics

Standard deviation1.5684288
Coefficient of variation (CV)0.10428382
Kurtosis0.20846544
Mean15.04
Median Absolute Deviation (MAD)1.01861
Skewness0.58920287
Sum12678.72
Variance2.4599688
MonotonicityNot monotonic
2023-06-14T22:51:44.051585image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13.89814258 1
 
0.1%
15.49946308 1
 
0.1%
14.39633369 1
 
0.1%
15.98555326 1
 
0.1%
14.92031336 1
 
0.1%
14.91174698 1
 
0.1%
14.60608602 1
 
0.1%
15.22045732 1
 
0.1%
15.34414649 1
 
0.1%
13.89115572 1
 
0.1%
Other values (833) 833
98.8%
ValueCountFrequency (%)
11.29106522 1
0.1%
11.40802741 1
0.1%
11.75333858 1
0.1%
11.75628066 1
0.1%
11.80816889 1
0.1%
11.97753668 1
0.1%
12.04084635 1
0.1%
12.04847574 1
0.1%
12.19336629 1
0.1%
12.2200489 1
0.1%
ValueCountFrequency (%)
20.03643036 1
0.1%
20.00690222 1
0.1%
19.90304351 1
0.1%
19.7612536 1
0.1%
19.6965158 1
0.1%
19.61674213 1
0.1%
19.60018992 1
0.1%
19.14537787 1
0.1%
19.14058089 1
0.1%
19.12857056 1
0.1%

threshold_optimised
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.14648082
Minimum0.05
Maximum0.31666666
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:44.143419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.05
5-th percentile0.05
Q10.11666667
median0.15
Q30.18333334
95-th percentile0.25
Maximum0.31666666
Range0.26666666
Interquartile range (IQR)0.06666667

Descriptive statistics

Standard deviation0.055134901
Coefficient of variation (CV)0.37639671
Kurtosis-0.26131507
Mean0.14648082
Median Absolute Deviation (MAD)0.03333334
Skewness0.11827829
Sum123.48334
Variance0.0030398573
MonotonicityNot monotonic
2023-06-14T22:51:44.230983image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0.15 202
24.0%
0.11666667 191
22.7%
0.18333334 163
19.3%
0.05 89
10.6%
0.21666667 84
10.0%
0.083333336 64
 
7.6%
0.25 36
 
4.3%
0.28333333 12
 
1.4%
0.31666666 2
 
0.2%
ValueCountFrequency (%)
0.05 89
10.6%
0.083333336 64
 
7.6%
0.11666667 191
22.7%
0.15 202
24.0%
0.18333334 163
19.3%
0.21666667 84
10.0%
0.25 36
 
4.3%
0.28333333 12
 
1.4%
0.31666666 2
 
0.2%
ValueCountFrequency (%)
0.31666666 2
 
0.2%
0.28333333 12
 
1.4%
0.25 36
 
4.3%
0.21666667 84
10.0%
0.18333334 163
19.3%
0.15 202
24.0%
0.11666667 191
22.7%
0.083333336 64
 
7.6%
0.05 89
10.6%

size_optimised
Categorical

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
100
843 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters2529
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row100
2nd row100
3rd row100
4th row100
5th row100

Common Values

ValueCountFrequency (%)
100 843
100.0%

Length

2023-06-14T22:51:44.316602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:44.405707image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
100 843
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1686
66.7%
1 843
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2529
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1686
66.7%
1 843
33.3%

Most occurring scripts

ValueCountFrequency (%)
Common 2529
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1686
66.7%
1 843
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2529
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1686
66.7%
1 843
33.3%

porosity_optimised
Real number (ℝ)

Distinct10
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27249638
Minimum0.23
Maximum0.37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:44.477400image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.23
5-th percentile0.23
Q10.23
median0.2611111
Q30.3077778
95-th percentile0.35444444
Maximum0.37
Range0.14
Interquartile range (IQR)0.0777778

Descriptive statistics

Standard deviation0.043825672
Coefficient of variation (CV)0.1608303
Kurtosis-0.77306103
Mean0.27249638
Median Absolute Deviation (MAD)0.0311111
Skewness0.67647353
Sum229.71444
Variance0.0019206896
MonotonicityNot monotonic
2023-06-14T22:51:44.559689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.23 307
36.4%
0.24555555 82
 
9.7%
0.27666667 80
 
9.5%
0.29222223 77
 
9.1%
0.3077778 69
 
8.2%
0.2611111 63
 
7.5%
0.32333332 57
 
6.8%
0.35444444 44
 
5.2%
0.33888888 34
 
4.0%
0.37 30
 
3.6%
ValueCountFrequency (%)
0.23 307
36.4%
0.24555555 82
 
9.7%
0.2611111 63
 
7.5%
0.27666667 80
 
9.5%
0.29222223 77
 
9.1%
0.3077778 69
 
8.2%
0.32333332 57
 
6.8%
0.33888888 34
 
4.0%
0.35444444 44
 
5.2%
0.37 30
 
3.6%
ValueCountFrequency (%)
0.37 30
 
3.6%
0.35444444 44
 
5.2%
0.33888888 34
 
4.0%
0.32333332 57
 
6.8%
0.3077778 69
 
8.2%
0.29222223 77
 
9.1%
0.27666667 80
 
9.5%
0.2611111 63
 
7.5%
0.24555555 82
 
9.7%
0.23 307
36.4%

defect_family
Categorical

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size6.7 KiB
CG_family
576 
Melt_family
267 

Length

Max length11
Median length9
Mean length9.633452
Min length9

Characters and Unicode

Total characters8121
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCG_family
2nd rowCG_family
3rd rowCG_family
4th rowCG_family
5th rowCG_family

Common Values

ValueCountFrequency (%)
CG_family 576
68.3%
Melt_family 267
31.7%

Length

2023-06-14T22:51:44.670462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-14T22:51:44.788743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
cg_family 576
68.3%
melt_family 267
31.7%

Most occurring characters

ValueCountFrequency (%)
l 1110
13.7%
_ 843
10.4%
f 843
10.4%
a 843
10.4%
m 843
10.4%
i 843
10.4%
y 843
10.4%
C 576
7.1%
G 576
7.1%
M 267
 
3.3%
Other values (2) 534
6.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5859
72.1%
Uppercase Letter 1419
 
17.5%
Connector Punctuation 843
 
10.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
l 1110
18.9%
f 843
14.4%
a 843
14.4%
m 843
14.4%
i 843
14.4%
y 843
14.4%
e 267
 
4.6%
t 267
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
C 576
40.6%
G 576
40.6%
M 267
18.8%
Connector Punctuation
ValueCountFrequency (%)
_ 843
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7278
89.6%
Common 843
 
10.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
l 1110
15.3%
f 843
11.6%
a 843
11.6%
m 843
11.6%
i 843
11.6%
y 843
11.6%
C 576
7.9%
G 576
7.9%
M 267
 
3.7%
e 267
 
3.7%
Common
ValueCountFrequency (%)
_ 843
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
l 1110
13.7%
_ 843
10.4%
f 843
10.4%
a 843
10.4%
m 843
10.4%
i 843
10.4%
y 843
10.4%
C 576
7.1%
G 576
7.1%
M 267
 
3.3%
Other values (2) 534
6.6%

area
Real number (ℝ)

Distinct9
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.330961
Minimum4
Maximum33
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:44.871909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q14
median5
Q310.5
95-th percentile33
Maximum33
Range29
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation10.010211
Coefficient of variation (CV)0.96895254
Kurtosis1.0869951
Mean10.330961
Median Absolute Deviation (MAD)1
Skewness1.6460033
Sum8709
Variance100.20432
MonotonicityNot monotonic
2023-06-14T22:51:44.966412image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 389
46.1%
33 127
 
15.1%
9 113
 
13.4%
10.5 85
 
10.1%
5 54
 
6.4%
8.75 40
 
4.7%
6 15
 
1.8%
22 10
 
1.2%
12.25 10
 
1.2%
ValueCountFrequency (%)
4 389
46.1%
5 54
 
6.4%
6 15
 
1.8%
8.75 40
 
4.7%
9 113
 
13.4%
10.5 85
 
10.1%
12.25 10
 
1.2%
22 10
 
1.2%
33 127
 
15.1%
ValueCountFrequency (%)
33 127
 
15.1%
22 10
 
1.2%
12.25 10
 
1.2%
10.5 85
 
10.1%
9 113
 
13.4%
8.75 40
 
4.7%
6 15
 
1.8%
5 54
 
6.4%
4 389
46.1%

area_calculated
Real number (ℝ)

Distinct278
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean588.82681
Minimum198
Maximum1716
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:45.068102image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile308
Q1400
median494
Q3713.5
95-th percentile1176
Maximum1716
Range1518
Interquartile range (IQR)313.5

Descriptive statistics

Standard deviation276.38696
Coefficient of variation (CV)0.46938582
Kurtosis1.7593985
Mean588.82681
Median Absolute Deviation (MAD)122
Skewness1.4524926
Sum496381
Variance76389.749
MonotonicityNot monotonic
2023-06-14T22:51:45.310698image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
420 19
 
2.3%
364 18
 
2.1%
448 15
 
1.8%
405 14
 
1.7%
486 14
 
1.7%
504 13
 
1.5%
378 12
 
1.4%
432 12
 
1.4%
476 12
 
1.4%
468 11
 
1.3%
Other values (268) 703
83.4%
ValueCountFrequency (%)
198 1
 
0.1%
224 1
 
0.1%
231 1
 
0.1%
232 1
 
0.1%
234 1
 
0.1%
242 1
 
0.1%
243 1
 
0.1%
252 1
 
0.1%
253 1
 
0.1%
264 3
0.4%
ValueCountFrequency (%)
1716 1
0.1%
1656 1
0.1%
1591 1
0.1%
1540 1
0.1%
1530 1
0.1%
1488 1
0.1%
1485 1
0.1%
1476 1
0.1%
1472 1
0.1%
1452 2
0.2%

length_width_ratio
Real number (ℝ)

Distinct8
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0551422
Minimum0.72727273
Maximum1.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:45.408030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.72727273
5-th percentile1
Q11
median1
Q31.0909091
95-th percentile1.4
Maximum1.5
Range0.77272727
Interquartile range (IQR)0.090909091

Descriptive statistics

Standard deviation0.1309622
Coefficient of variation (CV)0.12411806
Kurtosis2.879645
Mean1.0551422
Median Absolute Deviation (MAD)0
Skewness1.2326769
Sum889.48485
Variance0.017151098
MonotonicityNot monotonic
2023-06-14T22:51:45.489686image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
1 512
60.7%
1.090909091 127
 
15.1%
1.166666667 85
 
10.1%
1.4 40
 
4.7%
0.8 30
 
3.6%
1.25 24
 
2.8%
1.5 15
 
1.8%
0.7272727273 10
 
1.2%
ValueCountFrequency (%)
0.7272727273 10
 
1.2%
0.8 30
 
3.6%
1 512
60.7%
1.090909091 127
 
15.1%
1.166666667 85
 
10.1%
1.25 24
 
2.8%
1.4 40
 
4.7%
1.5 15
 
1.8%
ValueCountFrequency (%)
1.5 15
 
1.8%
1.4 40
 
4.7%
1.25 24
 
2.8%
1.166666667 85
 
10.1%
1.090909091 127
 
15.1%
1 512
60.7%
0.8 30
 
3.6%
0.7272727273 10
 
1.2%
Distinct261
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.64499771
Minimum0.27586207
Maximum2.6666667
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:45.597448image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.27586207
5-th percentile0.44827586
Q10.54285714
median0.625
Q30.71428571
95-th percentile0.90290323
Maximum2.6666667
Range2.3908046
Interquartile range (IQR)0.17142857

Descriptive statistics

Standard deviation0.16183106
Coefficient of variation (CV)0.25090175
Kurtosis31.295495
Mean0.64499771
Median Absolute Deviation (MAD)0.083333333
Skewness3.2360222
Sum543.73307
Variance0.026189291
MonotonicityNot monotonic
2023-06-14T22:51:45.712674image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 37
 
4.4%
0.5 30
 
3.6%
0.5555555556 16
 
1.9%
0.75 14
 
1.7%
0.5357142857 13
 
1.5%
0.625 13
 
1.5%
0.5714285714 13
 
1.5%
0.6 13
 
1.5%
0.6923076923 12
 
1.4%
0.5384615385 12
 
1.4%
Other values (251) 670
79.5%
ValueCountFrequency (%)
0.275862069 1
 
0.1%
0.2954545455 1
 
0.1%
0.3214285714 1
 
0.1%
0.3333333333 3
0.4%
0.3404255319 1
 
0.1%
0.3448275862 1
 
0.1%
0.3461538462 1
 
0.1%
0.35 1
 
0.1%
0.3617021277 1
 
0.1%
0.3684210526 1
 
0.1%
ValueCountFrequency (%)
2.666666667 1
0.1%
1.666666667 1
0.1%
1.411764706 1
0.1%
1.4 1
0.1%
1.25 1
0.1%
1.233333333 1
0.1%
1.2 1
0.1%
1.107142857 1
0.1%
1.105263158 1
0.1%
1.068965517 2
0.2%

size_length_width_ratio
Real number (ℝ)

Distinct13
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.263404
Minimum4.5454545
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:45.804580image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4.5454545
5-th percentile4.5454545
Q116.666667
median30
Q337.5
95-th percentile50
Maximum50
Range45.454545
Interquartile range (IQR)20.833333

Descriptive statistics

Standard deviation13.484438
Coefficient of variation (CV)0.51343069
Kurtosis-1.200815
Mean26.263404
Median Absolute Deviation (MAD)7.7777778
Skewness-0.20320858
Sum22140.05
Variance181.83006
MonotonicityNot monotonic
2023-06-14T22:51:45.889532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
37.5 343
40.7%
4.545454545 88
 
10.4%
16.66666667 76
 
9.0%
30 54
 
6.4%
14.28571429 50
 
5.9%
50 46
 
5.5%
17.14285714 40
 
4.7%
6.060606061 39
 
4.6%
22.22222222 37
 
4.4%
19.04761905 35
 
4.2%
Other values (3) 35
 
4.2%
ValueCountFrequency (%)
4.545454545 88
10.4%
6.060606061 39
4.6%
6.818181818 10
 
1.2%
12.24489796 10
 
1.2%
14.28571429 50
5.9%
16.66666667 76
9.0%
17.14285714 40
4.7%
19.04761905 35
 
4.2%
22.22222222 37
4.4%
25 15
 
1.8%
ValueCountFrequency (%)
50 46
 
5.5%
37.5 343
40.7%
30 54
 
6.4%
25 15
 
1.8%
22.22222222 37
 
4.4%
19.04761905 35
 
4.2%
17.14285714 40
 
4.7%
16.66666667 76
 
9.0%
14.28571429 50
 
5.9%
12.24489796 10
 
1.2%
Distinct767
Distinct (%)91.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62035847
Minimum0.39795918
Maximum0.85714286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.7 KiB
2023-06-14T22:51:45.992596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0.39795918
5-th percentile0.48667659
Q10.57522374
median0.62544803
Q30.66966306
95-th percentile0.73762254
Maximum0.85714286
Range0.45918367
Interquartile range (IQR)0.094439313

Descriptive statistics

Standard deviation0.075336485
Coefficient of variation (CV)0.12144025
Kurtosis0.07551008
Mean0.62035847
Median Absolute Deviation (MAD)0.047171019
Skewness-0.28926002
Sum522.96219
Variance0.0056755859
MonotonicityNot monotonic
2023-06-14T22:51:46.105795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6666666667 7
 
0.8%
0.625 6
 
0.7%
0.6363636364 5
 
0.6%
0.6 4
 
0.5%
0.725 3
 
0.4%
0.6111111111 3
 
0.4%
0.65625 3
 
0.4%
0.5925925926 3
 
0.4%
0.5884773663 3
 
0.4%
0.6166666667 3
 
0.4%
Other values (757) 803
95.3%
ValueCountFrequency (%)
0.3979591837 1
0.1%
0.4007352941 1
0.1%
0.4019851117 1
0.1%
0.40378198 1
0.1%
0.4071428571 1
0.1%
0.4186666667 1
0.1%
0.4234375 1
0.1%
0.4319419238 1
0.1%
0.4320987654 1
0.1%
0.4347426471 1
0.1%
ValueCountFrequency (%)
0.8571428571 1
0.1%
0.8260869565 1
0.1%
0.8232323232 1
0.1%
0.8104575163 1
0.1%
0.803030303 1
0.1%
0.7885714286 1
0.1%
0.7878787879 1
0.1%
0.7804232804 1
0.1%
0.7801724138 1
0.1%
0.775210084 1
0.1%

Interactions

2023-06-14T22:51:38.373452image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.276568image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.887021image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.437718image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.913443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.536677image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.074136image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.445901image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.822335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.504194image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.943811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.331939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.982140image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.493390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.893189image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.472725image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.416905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.991735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.546379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.017145image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.639040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.169264image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.546610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.925672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.610969image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.042450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.438541image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.095351image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.591055image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.992394image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.572776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.536134image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.089650image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.645868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.121864image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.741675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.263527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.638782image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.027952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.709247image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.136390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.541732image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.201182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.684624image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.087682image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.676040image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.654504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.191182image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.749859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.221629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.842567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.358856image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.732907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.142935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.809003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.230868image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.644271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.303939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.782848image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.180802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.772813image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.759218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.287790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.852525image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.319335image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.939507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.451528image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.826367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.246435image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.905900image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.327361image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.742193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.408809image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.885098image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.274479image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.862896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.856291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.383456image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.948100image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.411337image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.027847image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.542776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.915527image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.347162image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.998328image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.416623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.837358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.512019image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.978802image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.361130image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.950558image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:17.948596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.477621image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.040368image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.511523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.114083image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.626380image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.002174image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.443881image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.087443image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.505786image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.925486image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.605133image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.069520image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.446155image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.049367image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.054635image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.570873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.135721image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.615608image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.198684image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.711819image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.090157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.539706image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.177348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.590271image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.015873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.701552image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.158156image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.531384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.149315image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.176069image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.665033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.231978image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.724738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.290840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.805543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.180206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.638290image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.268948image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.683961image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.111562image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.805276image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.249473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.624057image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.282794image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.284771image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.766689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.332661image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.828977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.530198image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.899719image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.272895image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.732896image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.364138image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.775676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.212458image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.907603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.342642image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.717567image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.388229image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.381425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.857391image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.422381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:22.951814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.617141image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:25.992178image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.357417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:28.825230image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.452957image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.865327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.305258image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:34.999152image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.434266image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:37.927208image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.499491image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.486252image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:19.959593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.524277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.101871image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.712872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.087826image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.451629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.085935image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.555415image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:31.960774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.563939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.102554image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.529905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.019097image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.688257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.595225image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.064071image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.632787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.227821image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.813587image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.186524image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.558595image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.191861image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.656061image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.059409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.676300image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.204317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.631218image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.118345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.779480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.697049image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.254663image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.725954image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.336341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.902262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.273654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.645244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.299646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.750185image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.147066image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.775993image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.301143image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.715393image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.202106image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:39.877774image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:18.789703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:20.343284image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:21.817943image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:23.431495image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:24.988207image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:26.360176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:27.730041image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:29.404873image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:30.844094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:32.236304image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:33.877095image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:35.395428image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:36.803045image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-14T22:51:38.286850image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-14T22:51:46.217144image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
lengthmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedporosity_optimisedareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculatedscanner_iddefect_typeproductdetection_modewidththicknessdefect_family
length1.000-0.6080.600-0.1460.4750.5740.531-0.6340.2620.9690.5780.698-0.049-0.9470.0480.1310.9620.2010.2100.6960.7630.988
mean_intensity_calculated-0.6081.000-0.5760.382-0.341-0.528-0.7770.827-0.057-0.586-0.483-0.2930.1470.583-0.2530.3180.4990.1300.1300.4800.1820.369
size_calculated0.600-0.5761.000-0.0980.8020.8470.269-0.8330.2100.5740.9440.3380.107-0.481-0.0440.2560.5270.2380.4080.4810.1660.355
porosity_calculated-0.1460.382-0.0981.0000.1980.030-0.4700.2330.197-0.1350.1340.0090.2370.216-0.8160.1210.1430.2630.2620.1600.1350.114
length_calculated0.475-0.3410.8020.1981.0000.5580.058-0.6230.2190.4590.8910.3010.580-0.363-0.4120.2460.3490.1990.3220.2950.0830.154
width_calculated0.574-0.5280.8470.0300.5581.0000.263-0.7140.2000.5470.8510.316-0.283-0.461-0.1410.1090.4870.1110.3490.4430.0710.255
thickness_calculated0.531-0.7770.269-0.4700.0580.2631.000-0.537-0.0070.5260.1720.234-0.235-0.5770.3630.2210.3440.4410.1460.3550.2100.206
threshold_optimised-0.6340.827-0.8330.233-0.623-0.714-0.5371.000-0.128-0.603-0.759-0.358-0.0070.547-0.0770.1240.5290.0440.2540.4860.1960.393
porosity_optimised0.262-0.0570.2100.1970.2190.200-0.007-0.1281.0000.3010.2310.1070.051-0.260-0.1610.0800.1730.0590.1710.1760.0740.225
area0.969-0.5860.574-0.1350.4590.5470.526-0.6030.3011.0000.5520.565-0.043-0.9670.0460.1420.8550.2030.2320.7090.6170.947
area_calculated0.578-0.4830.9440.1340.8910.8510.172-0.7590.2310.5521.0000.3510.201-0.451-0.3250.1960.4620.2400.4130.4080.0980.272
length_width_ratio0.698-0.2930.3380.0090.3010.3160.234-0.3580.1070.5650.3511.0000.007-0.549-0.0520.0960.7630.2180.2340.7380.9480.680
length_width_ratio_calculated-0.0490.1470.1070.2370.580-0.283-0.235-0.0070.051-0.0430.2010.0071.0000.073-0.3790.1770.0700.1380.0710.0510.0000.058
size_length_width_ratio-0.9470.583-0.4810.216-0.363-0.461-0.5770.547-0.260-0.967-0.451-0.5490.0731.000-0.1190.3650.7910.4010.6990.8910.6150.986
size_length_width_ratio_calculated0.048-0.253-0.044-0.816-0.412-0.1410.363-0.077-0.1610.046-0.325-0.052-0.379-0.1191.0000.0840.0790.2230.2280.0760.1150.106
scanner_id0.1310.3180.2560.1210.2460.1090.2210.1240.0800.1420.1960.0960.1770.3650.0841.0000.0000.2600.2960.1020.0900.000
defect_type0.9620.4990.5270.1430.3490.4870.3440.5290.1730.8550.4620.7630.0700.7910.0790.0001.0000.1550.2210.7620.5590.999
product0.2010.1300.2380.2630.1990.1110.4410.0440.0590.2030.2400.2180.1380.4010.2230.2600.1551.0000.3450.2030.1270.148
detection_mode0.2100.1300.4080.2620.3220.3490.1460.2540.1710.2320.4130.2340.0710.6990.2280.2960.2210.3451.0000.2720.1650.118
width0.6960.4800.4810.1600.2950.4430.3550.4860.1760.7090.4080.7380.0510.8910.0760.1020.7620.2030.2721.0000.5180.896
thickness0.7630.1820.1660.1350.0830.0710.2100.1960.0740.6170.0980.9480.0000.6150.1150.0900.5590.1270.1650.5181.0000.683
defect_family0.9880.3690.3550.1140.1540.2550.2060.3930.2250.9470.2720.6800.0580.9860.1060.0000.9990.1480.1180.8960.6831.000
2023-06-14T22:51:46.441220image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
lengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedsize_optimisedporosity_optimisedareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
length1.0000.9650.141-0.7150.807-0.2150.5420.7600.634-0.702NaN0.1900.9800.7280.318-0.091-0.8960.105
width0.9651.000-0.027-0.7320.835-0.2450.5490.7780.644-0.702NaN0.1950.9850.7470.073-0.100-0.8680.127
thickness0.141-0.0271.000-0.019-0.0510.0820.019-0.0210.047-0.071NaN0.023-0.028-0.0190.7150.032-0.309-0.068
mean_intensity_calculated-0.715-0.732-0.0191.000-0.6870.393-0.394-0.644-0.8040.850NaN-0.033-0.725-0.578-0.0810.1810.636-0.265
size_calculated0.8070.835-0.051-0.6871.000-0.1380.8000.8920.440-0.794NaN0.1660.8390.9530.0820.019-0.6280.007
porosity_calculated-0.215-0.2450.0820.393-0.1381.0000.245-0.021-0.4800.234NaN0.188-0.2440.1130.0830.2850.214-0.840
length_calculated0.5420.5490.019-0.3940.8000.2451.0000.5990.114-0.606NaN0.2100.5500.8980.1120.530-0.409-0.428
width_calculated0.7600.778-0.021-0.6440.892-0.0210.5991.0000.415-0.746NaN0.1670.7820.8760.099-0.303-0.591-0.084
thickness_calculated0.6340.6440.047-0.8040.440-0.4800.1140.4151.000-0.587NaN-0.0050.6420.3010.064-0.269-0.5990.374
threshold_optimised-0.702-0.702-0.0710.850-0.7940.234-0.606-0.746-0.5871.000NaN-0.095-0.701-0.734-0.1380.0450.585-0.092
size_optimisedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
porosity_optimised0.1900.1950.023-0.0330.1660.1880.2100.167-0.005-0.095NaN1.0000.1650.2010.0320.071-0.242-0.156
area0.9800.985-0.028-0.7250.839-0.2440.5500.7820.642-0.701NaN0.1651.0000.7490.158-0.100-0.8310.129
area_calculated0.7280.747-0.019-0.5780.9530.1130.8980.8760.301-0.734NaN0.2010.7491.0000.1000.131-0.557-0.268
length_width_ratio0.3180.0730.715-0.0810.0820.0830.1120.0990.064-0.138NaN0.0320.1580.1001.0000.035-0.329-0.083
length_width_ratio_calculated-0.091-0.1000.0320.1810.0190.2850.530-0.303-0.2690.045NaN0.071-0.1000.1310.0351.0000.085-0.391
size_length_width_ratio-0.896-0.868-0.3090.636-0.6280.214-0.409-0.591-0.5990.585NaN-0.242-0.831-0.557-0.3290.0851.000-0.110
size_length_width_ratio_calculated0.1050.127-0.068-0.2650.007-0.840-0.428-0.0840.374-0.092NaN-0.1560.129-0.268-0.083-0.391-0.1101.000
2023-06-14T22:51:46.662485image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
lengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedsize_optimisedporosity_optimisedareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
length1.0000.9220.382-0.6080.600-0.1460.4750.5740.531-0.634NaN0.2620.9690.5780.698-0.049-0.9470.048
width0.9221.0000.235-0.6240.589-0.1820.4430.5640.563-0.627NaN0.2930.9710.5500.398-0.070-0.9440.084
thickness0.3820.2351.000-0.0970.0980.0270.0700.0480.126-0.130NaN0.0150.3000.0750.5480.037-0.288-0.038
mean_intensity_calculated-0.608-0.624-0.0971.000-0.5760.382-0.341-0.528-0.7770.827NaN-0.057-0.586-0.483-0.2930.1470.583-0.253
size_calculated0.6000.5890.098-0.5761.000-0.0980.8020.8470.269-0.833NaN0.2100.5740.9440.3380.107-0.481-0.044
porosity_calculated-0.146-0.1820.0270.382-0.0981.0000.1980.030-0.4700.233NaN0.197-0.1350.1340.0090.2370.216-0.816
length_calculated0.4750.4430.070-0.3410.8020.1981.0000.5580.058-0.623NaN0.2190.4590.8910.3010.580-0.363-0.412
width_calculated0.5740.5640.048-0.5280.8470.0300.5581.0000.263-0.714NaN0.2000.5470.8510.316-0.283-0.461-0.141
thickness_calculated0.5310.5630.126-0.7770.269-0.4700.0580.2631.000-0.537NaN-0.0070.5260.1720.234-0.235-0.5770.363
threshold_optimised-0.634-0.627-0.1300.827-0.8330.233-0.623-0.714-0.5371.000NaN-0.128-0.603-0.759-0.358-0.0070.547-0.077
size_optimisedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
porosity_optimised0.2620.2930.015-0.0570.2100.1970.2190.200-0.007-0.128NaN1.0000.3010.2310.1070.051-0.260-0.161
area0.9690.9710.300-0.5860.574-0.1350.4590.5470.526-0.603NaN0.3011.0000.5520.565-0.043-0.9670.046
area_calculated0.5780.5500.075-0.4830.9440.1340.8910.8510.172-0.759NaN0.2310.5521.0000.3510.201-0.451-0.325
length_width_ratio0.6980.3980.548-0.2930.3380.0090.3010.3160.234-0.358NaN0.1070.5650.3511.0000.007-0.549-0.052
length_width_ratio_calculated-0.049-0.0700.0370.1470.1070.2370.580-0.283-0.235-0.007NaN0.051-0.0430.2010.0071.0000.073-0.379
size_length_width_ratio-0.947-0.944-0.2880.583-0.4810.216-0.363-0.461-0.5770.547NaN-0.260-0.967-0.451-0.5490.0731.000-0.119
size_length_width_ratio_calculated0.0480.084-0.038-0.253-0.044-0.816-0.412-0.1410.363-0.077NaN-0.1610.046-0.325-0.052-0.379-0.1191.000
2023-06-14T22:51:46.873409image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
lengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedsize_optimisedporosity_optimisedareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
length1.0000.8560.327-0.4790.479-0.1070.3800.4620.411-0.546NaN0.2100.9270.4580.545-0.038-0.8780.035
width0.8561.0000.195-0.5010.472-0.1380.3570.4570.445-0.543NaN0.2380.9400.4370.298-0.053-0.8770.064
thickness0.3270.1951.000-0.0760.0750.0220.0570.0370.100-0.110NaN0.0120.2380.0580.5200.029-0.230-0.031
mean_intensity_calculated-0.479-0.501-0.0761.000-0.4050.261-0.238-0.376-0.5830.687NaN-0.042-0.451-0.332-0.2190.1000.443-0.170
size_calculated0.4790.4720.075-0.4051.000-0.0630.6370.6820.171-0.686NaN0.1520.4490.8040.2460.076-0.350-0.031
porosity_calculated-0.107-0.1380.0220.261-0.0631.0000.1410.027-0.3250.171NaN0.143-0.0980.1000.0050.1590.154-0.624
length_calculated0.3800.3570.057-0.2380.6370.1411.0000.4110.033-0.491NaN0.1620.3610.7460.2280.430-0.271-0.297
width_calculated0.4620.4570.037-0.3760.6820.0270.4111.0000.172-0.582NaN0.1480.4290.6830.243-0.201-0.335-0.100
thickness_calculated0.4110.4450.100-0.5830.171-0.3250.0330.1721.000-0.407NaN-0.0050.4020.1030.180-0.159-0.4430.248
threshold_optimised-0.546-0.543-0.1100.687-0.6860.171-0.491-0.582-0.4071.000NaN-0.101-0.506-0.604-0.284-0.0040.437-0.055
size_optimisedNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.000NaNNaNNaNNaNNaNNaNNaN
porosity_optimised0.2100.2380.012-0.0420.1520.1430.1620.148-0.005-0.101NaN1.0000.2360.1670.0880.037-0.197-0.115
area0.9270.9400.238-0.4510.449-0.0980.3610.4290.402-0.506NaN0.2361.0000.4290.408-0.032-0.9160.034
area_calculated0.4580.4370.058-0.3320.8040.1000.7460.6830.103-0.604NaN0.1670.4291.0000.2590.139-0.325-0.231
length_width_ratio0.5450.2980.520-0.2190.2460.0050.2280.2430.180-0.284NaN0.0880.4080.2591.0000.005-0.392-0.039
length_width_ratio_calculated-0.038-0.0530.0290.1000.0760.1590.430-0.201-0.159-0.004NaN0.037-0.0320.1390.0051.0000.053-0.261
size_length_width_ratio-0.878-0.877-0.2300.443-0.3500.154-0.271-0.335-0.4430.437NaN-0.197-0.916-0.325-0.3920.0531.000-0.085
size_length_width_ratio_calculated0.0350.064-0.031-0.170-0.031-0.624-0.297-0.1000.248-0.055NaN-0.1150.034-0.231-0.039-0.261-0.0851.000
2023-06-14T22:51:47.092672image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
scanner_iddatedefect_typeproductdetection_modelengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedporosity_optimiseddefect_familyareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
scanner_id1.0000.4790.0000.1580.4490.1820.0840.1360.4160.3360.1580.3220.1420.2900.1240.1050.0000.1170.2540.0900.1660.4870.108
date0.4791.0000.6480.9390.8560.7830.7460.8690.4220.5740.4220.4200.4730.5870.5330.2360.5230.6100.5060.8920.4120.8930.351
defect_type0.0000.6481.0000.1640.3320.9970.8020.8850.6980.7240.2380.5370.6870.5300.6970.2851.0000.8690.6630.8440.1020.9760.128
product0.1580.9390.1641.0000.2110.4380.2610.1350.2150.3700.4020.3170.1840.5990.1020.1000.0890.2610.3720.3090.2030.5370.350
detection_mode0.4490.8560.3320.2111.0000.2920.2230.2480.1710.5320.3430.4220.4570.1910.2550.2240.1850.1900.5380.2190.0660.8780.301
length0.1820.7830.9970.4380.2921.0000.8060.8870.6800.6840.2940.4770.6420.5750.7090.2711.0000.9430.6230.9040.1100.8610.171
width0.0840.7460.8020.2610.2230.8061.0000.5910.8220.8230.3660.6080.7890.6910.6880.3990.7640.9580.7520.8370.0800.9330.180
thickness0.1360.8690.8850.1350.2480.8870.5911.0000.2990.2730.2230.1390.1190.3430.3010.1240.8790.6830.1630.9520.0000.9090.192
mean_intensity_calculated0.4160.4220.6980.2150.1710.6800.8220.2991.0000.7410.4780.4980.6830.8380.7690.2120.4820.8310.6610.6340.2490.6520.289
size_calculated0.3360.5740.7240.3700.5320.6840.8230.2730.7411.0000.4190.8150.8360.6480.7080.2450.4630.8420.9250.6340.1870.6810.302
porosity_calculated0.1580.4220.2380.4020.3430.2940.3660.2230.4780.4191.0000.3660.3610.5100.2330.2150.1500.3570.4730.3510.3330.3960.857
length_calculated0.3220.4200.5370.3170.4220.4770.6080.1390.4980.8150.3661.0000.6130.3890.5080.1990.2020.6220.8940.4280.5270.4970.535
width_calculated0.1420.4730.6870.1840.4570.6420.7890.1190.6830.8360.3610.6131.0000.6140.6500.2210.3350.8060.8330.5930.6200.6370.432
thickness_calculated0.2900.5870.5300.5990.1910.5750.6910.3430.8380.6480.5100.3890.6141.0000.5870.1680.2700.7040.5640.5320.4010.5570.421
threshold_optimised0.1240.5330.6970.1020.2550.7090.6880.3010.7690.7080.2330.5080.6500.5871.0000.1540.3940.6960.6250.6330.2540.6560.106
porosity_optimised0.1050.2360.2850.1000.2240.2710.3990.1240.2120.2450.2150.1990.2210.1680.1541.0000.2950.3140.2430.1240.1190.2590.152
defect_family0.0000.5231.0000.0890.1851.0000.7640.8790.4820.4630.1500.2020.3350.2700.3940.2951.0000.8240.3550.6330.0541.0000.135
area0.1170.6100.8690.2610.1900.9430.9580.6830.8310.8420.3570.6220.8060.7040.6960.3140.8241.0000.7830.8530.0820.8400.111
area_calculated0.2540.5060.6630.3720.5380.6230.7520.1630.6610.9250.4730.8940.8330.5640.6250.2430.3550.7831.0000.5570.1170.6200.525
length_width_ratio0.0900.8920.8440.3090.2190.9040.8370.9520.6340.6340.3510.4280.5930.5320.6330.1240.6330.8530.5571.0000.2170.8900.226
length_width_ratio_calculated0.1660.4120.1020.2030.0660.1100.0800.0000.2490.1870.3330.5270.6200.4010.2540.1190.0540.0820.1170.2171.0000.1660.378
size_length_width_ratio0.4870.8930.9760.5370.8780.8610.9330.9090.6520.6810.3960.4970.6370.5570.6560.2591.0000.8400.6200.8900.1661.0000.263
size_length_width_ratio_calculated0.1080.3510.1280.3500.3010.1710.1800.1920.2890.3020.8570.5350.4320.4210.1060.1520.1350.1110.5250.2260.3780.2631.000
2023-06-14T22:51:47.372540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
thicknessproductdefect_typescanner_iddetection_modewidthdefect_family
thickness1.0000.1270.5590.0900.1650.5180.683
product0.1271.0000.1550.2600.3450.2030.148
defect_type0.5590.1551.0000.0000.2210.7620.999
scanner_id0.0900.2600.0001.0000.2960.1020.000
detection_mode0.1650.3450.2210.2961.0000.2720.118
width0.5180.2030.7620.1020.2721.0000.896
defect_family0.6830.1480.9990.0000.1180.8961.000

Missing values

2023-06-14T22:51:40.037116image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-14T22:51:40.362814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

file_namescanner_iddatetimedefect_typeproductdetection_modelengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedsize_optimisedporosity_optimiseddefect_familyareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
0ok112009_2023-01-05-08-09-58-376137ok1120092023-01-0508:09:58CGEkla20manual2.02.51.00.1974012940.140351152813.8981430.1833331000.230000CG_family5.04200.80.53571430.00.700000
1ok112009_2023-01-05-08-10-54-621560ok1120092023-01-0508:10:54CGEkla20manual2.02.51.00.1968142840.149701162713.7354660.1833331000.292222CG_family5.04320.80.59259330.00.657407
2ok112009_2023-01-05-08-11-33-694801ok1120092023-01-0508:11:33CGEkla20manual2.02.51.00.1956353860.257692243214.3803820.1500001000.370000CG_family5.07680.80.75000030.00.502604
3ok112009_2023-01-05-08-12-12-745345ok1120092023-01-0508:12:12CGEkla20manual2.02.51.00.1769642950.219577182814.8679380.1500001000.307778CG_family5.05040.80.64285730.00.585317
4ok112009_2023-01-05-08-12-53-064465ok1120092023-01-0508:12:53CGEkla20manual2.02.51.00.1877502690.129450162414.6815360.1833331000.230000CG_family5.03840.80.66666730.00.700521
5ok112009_2023-01-05-08-13-23-744552ok1120092023-01-0508:13:23CGEkla20manual2.02.51.00.1978782430.170648192113.9527240.1833331000.276667CG_family5.03990.80.90476230.00.609023
6ok112009_2023-01-05-08-14-07-130031ok1120092023-01-0508:14:07CGEkla20manual2.02.51.00.1810132080.091703122314.8760080.1833331000.230000CG_family5.02760.80.52173930.00.753623
7ok112009_2023-01-05-08-14-47-436262ok1120092023-01-0508:14:47CGEkla20manual2.02.51.00.1713572680.146497162615.2803170.1500001000.292222CG_family5.04160.80.61538530.00.644231
8ok112009_2023-01-05-08-15-28-389419ok1120092023-01-0508:15:28CGEkla20manual2.02.51.00.1969312710.232295143214.3709040.1833331000.338889CG_family5.04480.80.43750030.00.604911
9ok112009_2023-01-05-08-16-00-277698ok1120092023-01-0508:16:00CGEkla20manual2.02.51.00.1971012370.188356182214.4398140.1833331000.261111CG_family5.03960.80.81818230.00.598485
file_namescanner_iddatetimedefect_typeproductdetection_modelengthwidththicknessmean_intensity_calculatedsize_calculatedporosity_calculatedlength_calculatedwidth_calculatedthickness_calculatedthreshold_optimisedsize_optimisedporosity_optimiseddefect_familyareaarea_calculatedlength_width_ratiolength_width_ratio_calculatedsize_length_width_ratiosize_length_width_ratio_calculated
833ok112013_2023-03-24_09-55-53-874860ok1120132023-03-2409:55:53MeltGrosPacific12manual3.53.01.40.1922212040.250000142716.2309270.2166671000.276667Melt_family10.53781.1666670.51851914.2857140.539683
834ok112013_2023-03-24_09-56-06-686946ok1120132023-03-2409:56:06MeltGrosPacific12manual3.53.01.40.1747192300.187279142716.8559740.1500001000.261111Melt_family10.53781.1666670.51851914.2857140.608466
835ok112013_2023-03-24_09-56-18-869271ok1120132023-03-2409:56:18MeltGrosPacific12manual3.53.01.40.1876751790.175115112516.4651230.2500001000.230000Melt_family10.52751.1666670.44000014.2857140.650909
836ok112013_2023-03-24_09-56-31-678527ok1120132023-03-2409:56:31MeltGrosPacific12manual3.53.01.40.1776611890.215768122417.0465470.2166671000.338889Melt_family10.52881.1666670.50000014.2857140.656250
837ok112013_2023-03-24_09-56-55-409510ok1120132023-03-2409:56:55MeltGrosPacific12manual3.53.01.40.1684332280.227119142917.4643600.1833331000.338889Melt_family10.54061.1666670.48275914.2857140.561576
838ok112013_2023-03-24_09-57-14-204262ok1120132023-03-2409:57:14MeltGrosPacific12manual3.53.01.40.1586672310.183746122718.1794740.1500001000.307778Melt_family10.53241.1666670.44444414.2857140.712963
839ok112013_2023-03-24_09-57-29-780777ok1120132023-03-2409:57:29MeltGrosPacific12manual3.53.01.40.1932712090.208333132616.0430680.2166671000.230000Melt_family10.53381.1666670.50000014.2857140.618343
840ok112013_2023-03-24_09-57-42-679290ok1120132023-03-2409:57:42MeltGrosPacific12manual3.53.01.40.1792102200.172932162316.8568530.1833331000.245556Melt_family10.53681.1666670.69565214.2857140.597826
841ok112013_2023-03-24_09-58-05-101256ok1120132023-03-2409:58:05MeltGrosPacific12manual3.53.01.40.1579032460.163265142818.2971130.1166671000.261111Melt_family10.53921.1666670.50000014.2857140.627551
842ok112013_2023-03-24_09-58-19-160242ok1120132023-03-2409:58:19MeltGrosPacific12manual3.53.01.40.1668441950.204082132417.5388650.1833331000.370000Melt_family10.53121.1666670.54166714.2857140.625000